Page 162 - Artificial Intelligence for the Internet of Everything

148 Artificial Intelligence for the Internet of Everything

The IoT changes a user’s normal perspective on how valuable informa-
tion is obtained. We have many, many sources potentially sending informa-
tion to a decision maker. This number of sources and amount of information
can be both good and bad. It can be good by enabling us to reduce the uncer-
tainty of some random variables. That is, one may be able to replace a con-
tinuous random variable with a large region of support, ideally with a Dirac
delta distribution, where we would precisely know the information. That
would be the ideal case and is discussed in the later sections of Howard
(1966). However, in the following section we will illustrate some mathe-
matical differences in what Howard did, and discuss our findings with regard
to perfect information (clairvoyance), which are also different.
The IoT can also have negative effects; particularly when it comes to var-
ied sources of potentially valuable decision-relevant information. Since the
IoT is a huge conglomeration of processing and sensing devices, it is possible,
and perhaps even likely, that contradictory information is obtained. Further-
more, the IoT will also be artificially intelligent itself (Elvy, 2017; Etzion,
2015). Machine learning algorithms are currently employed in the IoT at
the local device and global usage levels (Ren & Gu, 2015). Much of the
machine-learning approaches are implemented to provide the IoT with
decision-making autonomy. In the next dimension of system intelligence,
the IoT has already begun incorporating technologies to add increasing/
improving autonomic or self-star (self-*) behaviors. Self-* behaviors are
those characteristics that form self-awareness and include self-organization,
self-adaptation, and self-protection. The dependence on AI in IoT, in this
context, is apparent. However, the implications for AI-enabled self-* behav-
iors to impact information value are less clear. Nonetheless, there is ample
documentation in the literature about how AI can and will be employed as
a gatekeeper for information (Camerer, 2017; Conitzer, Sinnott-Armstrong,
Borg, Deng, & Kramer, 2017; Naseem & Ahmed, 2017).
The overwhelming number of devices and data that they provide has
already necessitated a need for machine learning (Witten, Frank, Hall, &
Pal, 2016). The IoT easily interconnects devices and the information
between them and with other objects and humans, facilitating the ability
to transfer data to them without human-to-computer or human-to-human
interaction. Reasoning capabilities stemming from machine learning and
also from exploiting other (potentially centralized) resources brings benefi-
cial effects in terms of system efficiency and dependability and adaptive phys-
ical and behavioral human-system interactions and collaborations for users